October 2014, IDC #AP2578213VE

Excerpt

Big Data Professional Services: Market Forecast and Key Players in Asia

Prabhitha Sheethal Dcruz Mayur Sahni

IDC OPINION

IDC believes that the Big Data market in Asia/Pacific (excluding Japan) or APEJ is still at nascent

stage and has yet to experience large-scale adoption. With very few early adopters able to justify their

investments, others are still struggling to address technology and business challenges. Nevertheless,

as these technologies evolve, businesses continue to show interest and actively research and explore

how Big Data and analytics (BDA) initiatives will fit into their organization. IDC has observed the

following trends in the region:

IDC expects that the Big Data services market in APEJ will grow from US$228.16 million in

2013 to US$644.75 million in 2017.

IDC's study reflected that only a small percentage of businesses in APEJ are actively pursuing

opportunities with Big Data and analytics. Most of these engagements are in the pilot stage

and hence ad hoc in nature, which implies that businesses in the region have not yet

developed an enterprisewide strategy for Big Data.

The huge investments and complexities associated with Big Data implementations are forcing

organizations to turn to professional services firms that will help them develop a clear business

case, identify data sources (internal and external), and help choose the right mix of

technologies and solutions for their organization. As such, organizations across APEJ are

showing significant interest in consulting-led engagements.

Majority of the current Big Data deployments do not adhere to the 4Vs — volume, variety,

value, and velocity. The bulk of these initiatives mostly involve internal/enterprise data and do

not include any external data, thus limiting the outcomes of Big Data projects. Businesses

continue to view BDA technologies as a hardware/software solution rather as a business

process solution. BDA projects should be followed by a business process change to reap the

true benefits derived from faster and more reliable technology solutions.

Hiring and retaining talent is a major challenge for all organizations across APEJ. Bulk of the

skills required for Big Data implementations are still mostly nonexistent in the marketplace.

IDC believes majority of the talent will be supplied by the vendor community, which is currently

investing in developing talent through trainings and alliances with universities.

The industry is currently vendor-driven and vendors are making significant investments in developing

and marketing their BDA services portfolio. While BDA initiatives can singly deliver business

outcomes, IDC recommends that businesses invest in other 3rd Platform technologies, such as cloud,

that will aid in the faster delivery of scalable solutions.

©2014 IDC #AP2578213VE 1

IN THIS EXCERPT

This is an excerpt from an ongoing report series on service providers offering Big Data services in

Asia/Pacific (excluding Japan) or APEJ. As part of the study, IDC Asia/Pacific profiled HP as one of the

key players in the region

Big Data Definition

The intelligent economy produces a constant stream of data that is being monitored and analyzed.

Social interactions, mobile devices, facilities, equipment, research and development (R&D),

simulations, and physical infrastructure all contribute to the flow. In aggregate, this is what is called Big

Data. IDC's Big Data technology and services document size and forecast the technology and services

for managing, analyzing, and accessing Big Data, not the data itself. However, for the purpose of this

document we have only included numbers for Big Data services. IDC defines Big Data technologies as

a new generation of technologies and architectures designed to economically extract value from very

large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis.

Following IDC's Big Data definition, we created criteria to determine whether a use case and

associated technology and services should be included in the Big Data market sizing.

Step 1 evaluates three scenarios:

Deployments in which the data collected is over 100 terabytes (TB) (we use data collected, not

stored, to account for the use of in-memory technology in which data may not be stored in a

disk.)

Deployments of ultrahigh-speed messaging technology for real-time streaming, data capture,

and monitoring (this scenario represents Big Data in motion — as opposed to Big Data at

rest.)

Deployments in which the data sets may not be very large today but are growing very rapidly

at a rate of 60% or more annually

Step 2 evaluates whether, for each of the three scenarios of step 1, technology is deployed on

dynamically adaptable infrastructure. This could be a scale-out or a non-scale-out infrastructure.

Step 3 evaluates two scenarios:

Deployments that include two or more data types or data sources

Deployments that include high-speed data sources such as clickstream tracking or monitoring

of machine-generated data

IDC's definition for Big Data, the market sizing criteria, and the three steps for evaluating use cases

are depicted in Figure 1.

©2014 IDC #AP2578213VE 2

Data is received

via ultrahigh-

speed

streaming

FIGURE 1

IDC's Technologies and Services Market Sizing Criteria

Source: IDC, 2014

SITUATION OVERVIEW

The Big Data Market Today

In the past 24 months, businesses in Asia have embarked on their journey toward the implementation

of 3rd Platform technologies. Although cloud and mobility solutions have made headway into most

organizations across APEJ, Big Data is yet to get its due respect. Early adoptions of BDA have been

mostly in silos with no enterprise support for measurement tools or methods to gauge outcomes and

effectiveness. In addition, the return on investments (ROI) associated with each of these deployments

seem to vary and remain elusive in most cases. This is mainly because the process of integrating

analytics across the business requires change in the business process too; guaranteeing ROI will

largely depend on how organizations have been able to use these high-speed technologies to drive

business and manage the change associated with them.

All enterprises own data, which until recently was collected mostly for reporting purposes. Big Data

projects are all about data and the actionable insights derived from this data. So logically the first step

in any Big Data project is to understand what data is available and what data will be used that could

©2014 IDC #AP2578213VE 3

help achieve business objectives. As organizations moved up the maturity curve they started using

analytics to derive value from this data. Traditionally different lines of business (LOBs) have used

different tools to analyze data with certain structure. Although this has worked well in the past with

structured small sets of data, the Big Data scenarios require analytical solutions that can run on large

sets of structured and unstructured data with high speed to provide real-time insights. Hence, the

traditional tools used for analyzing data may no longer be relevant in the case of Big Data. However, it

is not just the tools — even the traditional infrastructure will not be able to support Big Data. Hence,

Big Data deployments will require organizations to rework their entire architecture encompassing the

software and hardware aspects.

Enterprises today sit on heaps of data that is growing exponentially every day. Although the declining

costs in storage have helped to store all of this data irrespective of its usability, the key challenge with

Big Data deployments is to understand the quality of the data being analyzed. This is challenging

primarily due to two reasons. First, this data exists in pockets and there is no single centralized view

available for this data, hence the inability to correlate all of this data. Second, not all data is reliable

and accurate. Thus, ensuring that the data analyzed is of right quality and the technology has the

ability to extract value from data in real time are the key challenges in Big Data deployments.

As organizations across APEJ increase their spending in 3rd Platform technologies, Big Data

technologies are also climbing up the priority ladder in the CIOs' agenda. Despite CIOs acknowledging

the benefits associated with Big Data and analytics, very few of them have actually taken the plunge. A

recent IDC survey revealed that a large percentage of respondents have not yet implemented any Big

Data and analytics solutions in their organizations (see Figure 2). Well, this does not come as a

surprise as Big Data is currently vendor-driven and businesses have yet to develop a clear

understanding about Big Data and its associated technologies. With most of them following the

wait-and-watch approach, current deployments are restricted to the telco, finance, and public sectors.

©2014 IDC #AP2578213VE 4

FIGURE 2

Big Data Deployment Plans

Source: IDC APEJ IT Services Survey, 2013

Although actual deployments may be relatively low, there is a significant buzz surrounding Big Data

and its benefits. Everyone understands that the data they own can be put to use — to gain actionable

insights and make informed decisions. However, to reach a stage in which they can make sense of the

data they own is the first challenge.

The second most important aspect of Big Data deployments is the speed at which insights are

obtained that will help in decision making. However, to derive business outcomes obtained from these

high-speed technologies requires a major change in the way businesses operate. At present, business

users tend to make decisions based on the limited information they have or mostly from their previous

experiences. With Big Data, business users will be forced to change the way they make decisions —

from decisions based on intuitions to ones based on facts. Change management then becomes a

crucial part of determining the success of Big Data investments. The speed at which insights are

obtained will mean nothing if organizations do not change their business processes accordingly.

Having said thus, the major challenge lies in integrating these technologies with the existing enterprise

architecture. Businesses will increasingly turn to service providers that are able to provide them a road

map on how these technologies can be integrated into their current enterprise architecture. Big Data

comprises a set of genuinely new technologies and a convergence of more mature technologies. To

embrace Big Data fully, organizations need to be dedicated and determined to embrace a more

information-led culture. Big Data reflects both the complexity of data sets and the new technologies

and techniques to manage the growth, variability, and velocity in those data sets. An organization may

face a Big Data challenge if its current IT infrastructure is not fit to handle the increasing requirements

for availability and performance-related to growing volumes of data.

Currently implementing

(9.2%)

Not implemented

as yet, but plan to implement in the next 6 -

12 months (10.1%)

Already implemented and operating it right now

(15.6%)Not

implemented as yet, but plan to implement

after 12 months (22.1%)

Not implemented

as yet and neither plan to

implement (43.1%)

Total = 100%

©2014 IDC #AP2578213VE 5

Challenges

Big Data presents several opportunities to transform businesses. However, harnessing Big Data is not

an easy task and requires CIOs to work with multiple LOBs to develop enterprisewide architecture. Big

Data technologies are complex and are still evolving; hence there is a significant gap between

customers' understanding of these technologies and the technology limitations. Businesses

understand the competitive advantage Big Data can bring to the table but handling increasing volumes

of data to obtain real-time results that can be used to keep them ahead of the competition is very

challenging.

Customers

IT versus LOB funding. One of the primary challenges with Big Data projects is obtaining

sponsorship. Because most believe that Big Data is a solution waiting for a problem and Big

Data solutions are expensive, there is a constant tiff between the IT and the business

regarding who should be funding these projects since both these units have different

expectations from BDA projects. Hence, for BDA projects to be successful there has to be

continuous collaboration between the business and the IT.

Buy-in from multiple stakeholders. Since Big Data is all about data that is not owned by a

single team, Big Data and analytics projects require support from multiple LOBs. Also, since

the benefits of BDA projects are realized across multiple LOBs it is important to get buy-in

from multiple stakeholders.

Lack of business case. Although there has been an increase in the adoption rate of analytics

and Big Data technologies, ROI in most cases remains elusive. With most business cases

targeted at only acquiring sponsorship for the projects, businesses often miss out factoring in

all the pros and cons of their investments in the new technology. Hence, developing a clear

business case through an in-depth analysis of the problem and how businesses put together

resources and technologies to achieve goals becomes crucial. The business case should also

be able to identify costs for the overall management and integration of Big Data with the

enterprise's existing ecosystem. This will help businesses to get a realistic idea regarding the

ROI. Also, the business case should identify how to quantify the benefits through these

projects, and most importantly, on how these benefits will be realized by both IT and business.

Big Data use cases do not adhere to all the four Vs. As businesses try to compete against

each other they often give in to the hype of Big Data and fail to address all the four Vs —

volume, variety, value, and velocity — in their Big Data implementations. Most current use

cases are targeted at volume or at best include two Vs only — and often end up investing too

much in a solution that is no different from their present business analytics solution. Hence,

businesses must adhere to the four Vs to achieve business outcomes associated with the BDA

technologies.

Data management. Big Data is all about using data as an asset. Data could be both internal

within an organization and external to an organization. For some businesses, beginning with

internal data gives them a sense of control and security, although this may not completely

adhere to the definition of Big Data and the results may not be very different from their current

traditional relational database results. Although there is no dearth of data, the challenge is to

prioritize these different data sources to be analyzed while ensuring that data quality is

maintained.

©2014 IDC #AP2578213VE 6

Vendors

Lack of experienced vendors. As is the case with customers, most vendors while busy

marketing their Big Data capabilities have forgotten to invest in developing skills and

intellectual property that can aid them in their BDA projects. Customers always like to engage

with vendors that can show them successful references; however, in the case of BDA projects,

since the technology is still evolving it is hard to find experienced vendors.

Nonavailability of skilled resources. One of the key challenges with BDA projects has been the

lack of skilled human talent. The adoption of BDA is on the rise, but the supply of talent has

not kept pace with the rise, resulting in a big gap in the talent pool. The skill gap is across the

board — from data scientists to Hadoop architects to data warehousing specialists for various

platforms — which has a direct impact on vendors' ability to deliver quality BDA projects.

Attracting, developing, and retaining the right talent will be critical to all vendors as they vie for

greater market share in the BDA segment.

However, vendors are now investing heavily to bridge the gap between the adoption rates and

the talent supply. Some of the significant steps include: conducting training programs with

alliance partners on emerging tools, influencing the curriculum to expose students to this

growing opportunity, holding boot camps for deep technology skills; and providing solution

architect training to develop comprehensive solutions, and hands-on opportunities in emerging

areas such as visualization and Big Data, and so forth.

Lack of innovation in sales and delivery. Sales personnel who are used to selling traditional IT

products and services will also need to adapt and evolve to be able to sell 3rd Platform

technologies. Sales personnel need to be trained to sell to the business; they need to talk the

language of business solutions as opposed to the language of technology. Similarly, services

providers will have to improvise on their delivery (by means of tools and accelerators/agile

delivery models/cloud delivery models) to deliver faster and better services.

Technology

Technology maturity. In the early days, Big Data was synonymous with Hadoop. However, due

to advancements this has slowly changed to include a number of different technologies, which

are still evolving and yet to attain complete maturity. The lack of clarity regarding these

technologies has inhibited the large-scale adoption of BDA.

Privacy concerns. Security concerns surrounding data are currently inhibiting the adoption of

Big Data on a large scale. With growing concern among individuals regarding how their

personal data is being used, businesses have to be cautious about the way data is handled.

Big Data adopters will have to provide complete disclosures on how data is being collected,

stored, and shared. Though privacy concerns about data are not completely new, businesses

will have to work hard to pass on tangible benefits to the customers, which would lead to

lowering the overall expectation of privacy of users.

Cost of technology infrastructure. BDA technologies enable organizations to store and analyze

vast amounts of data irrespective of their structure, thus opening new avenues of business

opportunities. In an age in which businesses manage petabytes of data, the traditional data

warehouses become incompatible for managing the storage requirements. Although BDA

solutions are a perfect fit to the growing volume of data by providing the required flexibility and

scalability, these come at a high price; hence the high costs associated with the technology

are deterrent to the adoption of BDA on a large scale.

©2014 IDC #AP2578213VE 7

Big Data Services

IDC expects the Big Data technology and services market in APEJ to grow from US$548.4 million in

2012 to US$2.38 billion in 2017 at a compound annual growth rate (CAGR) of 34.1%. IDC takes into

account infrastructure (the fastest-growing segment of Big Data), software, and services to derive the

total revenue associated with Big Data technology and services. The total revenue associated with Big

Data services, specifically, is expected to grow from US$183.9 million in 2012 to US$644.8 million in

2017 at a CAGR of 28.5% during the forecast period.

TABLE 1

Asia/Pacific (Excluding Japan) Big Data Services Revenue, 2012—2017 ($M)

2012 2013 2014 2015 2016 2017 2012–2017

CAGR (%)

Big Data

Services

183.9 228.16 290.39 380.82 486.85 644.75 28.5

Source: IDC, 2013

Per IDC estimates in 2013, services constitute 30% of the total Big Data revenue. Project-based

services will be increasingly sought by customers to help them with their Big Data initiatives.

Customers will look to service providers that can offer them a road map — on how these initiatives will

be integrated with their existing software/hardware stack, how it will help them achieve their business

outcomes, what technology solutions suit their business best, and what data can be used to provide

actionable insights. For vendors, this means that having consulting capabilities is no longer optional

but is now a must-have offering among their 3rd Platform service offerings. With business taking a

front seat, these investments in business consulting capabilities, along with IT consulting capabilities,

will help vendors guide their customers and build a clear business case.

In IDC's discussion with vendors we noted that vendors are actively building their consulting

capabilities by building tools and use case repositories that will help them in their road map services

for Big Data. As mentioned earlier, data exists in silos in most organizations across APEJ. In some

organizations in which investments have been made to integrate enterprisewide data, the current

investments on analytics are still in silos. Big Data solutions, in contrast, will use data from a wide

variety of sources including mobile, sensors, videos, enterprise, and others. Enterprises have to take

caution in using this data derived from multiple sources to protect user privacy by using the right

security solutions and services.

Going forward, as enterprises integrate data, they will be faced with the challenge of using the right

data and tools. Service providers with the right expertise will be able to guide their customers on their

Big Data journey. Once these solutions have been deployed, integration across the existing enterprise

architecture will be another crucial factor that will decide the success of these investments.

©2014 IDC #AP2578213VE 8

The declining costs of storage have helped businesses to store huge amounts of data. But with the

ever increasing volumes of data the existing datacenter architectures will not be able to cope and will

need transformation. One of the key prerequisites of Big Data is storage that is able to handle huge

volumes of data, scale up or down based on requirements, and deliver analytical results at faster

speeds. As businesses move away from traditional analytics platforms to Big Data platforms, network

consulting and integration service providers will be increasingly sought out to address the networking

requirements of low latency and datacenter transformation.

The challenge does not end at having the right technology and the right data. Getting the right skills

also matters. Deployment of Big Data solutions requires people with the right mix of industry and

functional knowledge. With the growing concern over talent shortage, vendors will continue to

aggressively attract talent from competition. Several skills will be difficult to find in the marketplace and

a large number of vendors will increasingly compete to secure access to a small talent pool, often by

offering premium wages. To alleviate skills supply shortage, vendors have already invested in training

programs and workshops and built alliances with universities and government agencies to hone talent.

Big Data solutions at the moment are currently vendor-driven and are yet to enjoy mass adoption in

APEJ. As the adoption rates increase, customers will have a plethora of vendors to choose from. For

vendors this means a need to have differentiated services and solutions offerings to gain more market

share. As such, vendors will have to invest in building an ecosystem of partners, come up with

industry-specific solutions to differentiate themselves, and build competencies across technologies.

Vendor Profile

HP

The HP Analytics and Data Management Services Practice is crucial to the company's long-term

goal — to evolve into a key partner for its customers. Given the investments that HP has been making

in its Enterprise Services (ES) organization, the company is more strategically positioned in the market

today. What differentiates HP from its peer services organizations is its hardware and software

ownership.

However, although customers will expect HP to emphasize its own hardware and software products,

the company's strategy is to provide a best-of-breed approach, which will involve other technology

partners. Evidence of this is observed in the multiple technical alliances that HP has set up with Ab

Initio, Informatica, SAP, SAS, TIBCO, and Hortonworks in Asia/Pacific (including Japan) (APJ).

Another factor unique to HP is the fact that it does not emphasize a lot on external unstructured data.

Rather, HP shared that it is helping organizations realize the level of uncaptured data within the

organization, which has direct relevance for businesses for driving lower total cost of ownership (TCO)

through improved operational insights and higher ROI by mining business-relevant data for improving

investment potential. This is observed in the use cases that HP has undertaken (see Table 1).

©2014 IDC #AP2578213VE 9

TABLE 1

Common Use Cases for Big Data Services: HP

Vertical Use Case

Telecom Efficiencies in traditional data, content management, and

customer analytics

Banking and financial services Compliance solutions, information management solutions

including enterprise data warehouse (EDW), and customer

analytics

Public sector Efficiencies in traditional data, content management, records

management, and analytics for security and situational

awareness

Manufacturing Customer analytics and integrated supply chain analytics

Retail Customer analytics and store performance analytics

Source: IDC Big Data Services Vendor Interviews, 2014

As an organization that emphasizes Big Data as one of its strategic bets, HP is building out its

resource base in the region. The HP Analytics and Data Management Services Practice is part of HP

Enterprise Services and works in collaboration with HP Software for HP Autonomy and HP Vertical

products, and HP Hardware unit for infrastructure to deliver Big Data and analytics projects. Through

its industrialized delivery system, HP provides its analytics and data management services to 500

clients globally that are supported by its 1,200 global analytics professionals and more than 3,600

consultants worldwide. Within APJ, HP has the advantage of access to global delivery talent — in

India, China, and the Philippines — that delivers to clients across regions. HP claims that its Global

Analytics organization in India has over 1,000 data analysts/scientists.

HP provides advisory services through its HP Big Data Discovery Experience offering that aims to help

accelerate customers' entry and journey to Big Data analytics. The fact that these services are made

available through a cloud delivery model or on-premise make them an attractive proposition for first-

time customers who want to assess the value of data before investing.

In addition to the advisory services, other services offered by HP include: HP actionable analytics

services, HP Information Management Services, HP Information Governance Services, customer

analytics offerings, HP Interactive Media Command Center (IMCC), archival services, situational

awareness, and records management.

The HP HAVEn platform empowers HP's partners to build solutions for the analytics platform, helping

them to monetize Big Data. HP realizes that its partners are also important in helping the company to

innovate, as validated through HP's investments with its partners that range from HP ES developing

core capabilities on their partner technologies to developing joint solutions and platforms.

©2014 IDC #AP2578213VE 10

IDC believes that HP has emerged as a strong player in the Big Data professional services segment.

This is largely because HP is not only able to provide end-to-end services for analytics and data

management but also has a wide range of Big Data offerings across its hardware and software stacks.

In summary, IDC views HP as a key partner for businesses that are getting into their Big Data journey.

In cases in which customers have significant investments on the HP stack, the firm should be a de

facto partner.

FUTURE OUTLOOK

Despite the buzz surrounding Big Data technologies, these have not yet completely evolved and are

currently climbing up the technology maturity curve. Businesses, enthusiastic though they may be

about moving to the new world of Big Data technologies, cannot ignore their current software/hardware

stack for data warehousing. Traditional database architectures and Big Data architectures will continue

to coexist; managing this environment will prove to be increasingly complex for most enterprises,

hence they will turn to service providers that can help them implement and manage the new enterprise

architecture and has advanced analytical capabilities, manages huge volumes of data from various

sources, and is hosted within the company's datacenter or delivered via cloud. The low maturity level

of associated technologies, huge investments, elusive ROI, and the dearth of skills have been a major

driver for professional services engagements.

The current approach to Big Data and analytics is in silos, often ignoring the overall enterprise

architecture. Although this might work well in the short run for certain use cases in some enterprises to

drive the business, in the long run this approach will create significant challenges. Since 3rd Platform

technologies are disruptive in nature, it is essential to take a holistic approach spanning across

business, information, application, and technology in order to transform business (Figure 3).

©2014 IDC #AP2578213VE 11

FIGURE 3

Moving from Diagnostic Analytics to Predictive Analytics

Source: IDC, 2014

Going forward, the proliferation of sensor, mobile, wearable, and embedded devices (i.e., Internet of

Things or IoT) will become a significant driver of the Big Data market. The Internet of Things will create

huge amounts of data, most of it unstructured, providing opportunity to enterprises to analyze new data

sets that will help them in their decision-making process. To address data from the IoT, there will rise a

need to invest in technology for both data in motion and data at rest. As a result, enterprises will move

from traditional client-server architectures to high-speed server-to-server architectures. Hence,

enterprises will have to address the changing landscape of the datacenter and will end up increasing

their infrastructure investments. Adding to this are the complexities in managing user privacy. Although

on one hand, businesses will look at consolidating their application portfolio and centralizing their data;

on the other hand, this might make them more vulnerable to stealth and malware attacks. This will lead

to an increased demand for security applications spanning cloud and traditional datacenters. In light of

the increasing complexity associated with managing data storage and network, organizations will have

to engage in datacenter transformation. Since datacenter transformation is a costly affair, small and

medium-sized enterprises might turn to cloud service providers to provide them with the analytical

capabilities using cloud delivery models.

A key attribute of Big Data is velocity. Since speed is a critical factor, the decision and

recommendation solutions are not the only solutions that have to be automated — businesses will look

at having rapid deployment models with some automation for the development and testing of their Big

Data and analytics solutions. These solutions will utilize a mix of cognitive computing, rules

management, analytics, biometrics, rich media recognition software, and commercialized HPC

infrastructure. Vendors offering Big Data services should be able to offer rapid product development

Data collection and reporting

Basic analytics

Big Data —Advanced analytics

Run the

business

Grow the

business

Transform the

business

©2014 IDC #AP2578213VE 12

and testing cycles with shorter release cycles. IDC believes that the number of vendors offering Big

Data services will triple over the next three years (see IDC Big Data Predictions 2014: Beyond

Irrational Exuberance — Opportunities in the Big Data and Analytics Markets, IDC#WC20131211,

December 2013). With vendors competing against each other for greater market share, what vendors

can bring to the table will be a key differentiating factor alongside their intellectual property and

industrialized solutions. Improvements in automation, growth in appliances, and cloud deployments

will help vendors and businesses alleviate the problem of skill shortage to a certain extent.

The four pillars of the 3rd Platform — cloud, mobile, Big Data, and social — are disruptive. IDC

believes that as organizations transition from the 2nd Platform to the 3rd platform, cloud will likely

emerge as the delivery platform for these technologies. Professional services engagement will be

sought by organizations to substitute and complement their existing IT capabilities. Although service

providers will be able to help organizations in their 3rd Platform projects, the measurement of success

will depend on the organizations' capability to support these initiatives.

ESSENTIAL GUIDANCE

For CIOs

Before embarking on Big Data deployments, businesses should understand the actual benefits of Big

Data and not get carried away by the hype. The availability of a large number of platforms, tools, and

resources makes the task even complex.

Hence, it is imperative that businesses invest in the right tools and solutions and engage with a service

provider that is able to help them justify their investments.

Begin by evaluating the current data practices in your organization. This will include

understanding how data is currently handled in the organization, how decisions are made —

are they based on data or on intuition — and what are the current policies surrounding

data/information management.

Big Data initiatives will require CIOs to explore the business case associated with these new

technologies. This will categorically include two aspects. First, to understand the different data

sources that is accessible to the organization internally and externally:

Data sources that you know exist and are captured

Data sources that you know exist but are not captured

Data sources that may be valuable but have been left unexploited

Second, the use of the right analytics and technologies to gather actionable insights that are

delivered in real time. The success of BDA initiatives is also dependent on how organizations

are able to fine-tune their business processes to accommodate the business outcomes

brought about by these initiatives. Hence, BDA initiatives should also include a process

redesign plan.

For businesses taking the piecemeal approach, although the platform is able to cater to the

current requirements, consider as well how it will pan out in the future. Adequate data

©2014 IDC #AP2578213VE 13

governance and controls will need to be put in place to ensure that the same set of standards

and processes are maintained during cross-functional adoption.

BDA initiatives are different from the traditional enterprise resource management (ERM) and

customer relationship management (CRM) deployments. Hence, businesses will need to

understand that ROI calculation from these initiatives have to be done differently as compared

with traditional technology deployments.

BDA initiatives will require multiple teams to work in collaboration with each other; hence it is

imperative that before starting on the Big Data path there is an organizationwide commitment

and support for these initiatives.

IDC suggests that CIOs engage in proof of concepts (POCs) before actual adoption to

evaluate the benefits of BDA deployments. Consulting-led engagements in particular will help

businesses to derive better benefits from their engagements.

For Vendors

The current adoption rate of BDA is low, with most adoptions being in the evaluation stage. However,

IDC expects the BDA services market in APEJ to triple its value by 2017. To be able to tap into this

growing market, vendors will have to invest in people, process, and skills to position themselves as an

end-to-end services provider of Big Data services. As vendors continue to drive BDA adoption, IDC

recommends that vendors focus on the following areas:

Assessing clients' maturity and building the business case. Before beginning with any

implementation, vendors will have to gauge customers' maturity and current data practices,

and build a clear business case. Vendors will also have to develop benchmarking tools to

identify where the current organization is in relation to industry standards and future business

expectations, including organization, process, infrastructure, leadership, and risk/reward

assessments.

Building industry-specific expertise. Industries differ in their use and need of Big Data

technologies. Vendors need to develop industry-specific tools and knowledge to help their

customers. When mass adoption of BDA kicks in, intellectual property can go a long way in

differentiating a vendor from its immediate peers.

Creating innovations in sales and delivery. Most Big Data implementations will be funded by

business as opposed to IT units. Hence, vendors will have to train their sales force to ditch the

language of technology and talk the language of business solutions. Vendors will also need to

educate IT resources and to improve the IT team's influence on people in the business units.

In addition, vendors will also have to focus on shortening their development and test cycles

through reusable and repeatable frameworks to aid them in quicker and quality deliverables.

Investing in training to develop the required skill sets. Although cloud solutions and automation

might alleviate the scarcity of human skills, it is crucial that vendors have a qualified workforce

that can help guide customers through all stages of their implementations. Training programs,

partnerships with universities and education centers, and retaining good talent will help

vendors face powerful competitive headwinds.

©2014 IDC #AP2578213VE 14

Building partnerships to form an ecosystem of partners. Vendors will have to work on

partnering with product and solution firms that can support their customer base. These

partnerships will help the service provider be product/solution-agnostic and help customers

choose the right product for their enterprise architecture.

Building on consulting capabilities. 3rd Platform technologies transform business and have a

large impact on the business. Hence, customers will increasingly have consulting-led

engagements. It is no longer enough to have systems integration capabilities alone; consulting

capabilities are also becoming crucial in deciding the vendor's position in the marketplace.

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